MIT & Harvard Classes

Undergrad · Fall, Spring · 6 units

Build a programming foundation through Python, algorithms, testing, and debugging.

Prerequisites: None

Undergrad · Fall, Spring · 12 units

Study partial derivatives, gradients, optimization, and multiple integrals—the mathematical tools used throughout machine learning.

Prerequisites: Calculus I (GIR)

Undergrad · Spring · 4-0-8 units

Basic subject on matrix theory and linear algebra, emphasizing topics useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, singular value decomposition, and positive definite matrices. Applications to least-squares approximations, stability of differential equations, networks, Fourier transforms, and Markov processes. Uses linear algebra software.

Prerequisites: Calculus II (GIR)

Undergrad · Fall · 5-0-7 units

Introductory course in linear algebra and optimization, assuming no prior exposure to linear algebra and starting from the basics, including vectors, matrices, eigenvalues, singular values, and least squares. Covers the basics in optimization including convex optimization, linear/quadratic programming, gradient descent, and regularization, building on insights from linear algebra. Offered in the fall as an alternative to 18.06 (which now runs in the spring); credit cannot be received for both.

Prerequisites: Calculus II (GIR)

Undergrad · Spring · 4-0-8 units

A unified introduction to probability, Bayesian inference, and frequentist statistics. Topics include: combinatorics, random variables, (joint) distributions, covariance, central limit theorem; Bayesian updating, odds, posterior prediction; significance tests, confidence intervals, bootstrapping, regression. Students also develop computational skills and statistical thinking by using R to simulate, analyze, and visualize data.

Prerequisites: Calculus II (GIR)

Undergrad · Fall, Spring · 4-0-8 units

Probability spaces, random variables, distribution functions. Binomial, geometric, hypergeometric, Poisson distributions. Uniform, exponential, normal, gamma and beta distributions. Conditional probability, Bayes theorem, joint distributions. Chebyshev inequality, law of large numbers, and central limit theorem.

Prerequisites: Calculus II (GIR)

Undergrad · Fall, Spring · 12 units

Learn probabilistic modeling, inference, and random processes. Credit cannot also be received for 18.600.

Prerequisites: Calculus II (GIR)

Undergrad · Fall, Spring · 4-0-8 units

Introduction to the principles and algorithms of machine learning from an optimization perspective. Topics include linear and non-linear models for supervised, unsupervised, and reinforcement learning, with a focus on gradient-based methods and neural-network architectures. Previous experience with algorithms may be helpful.

Prerequisites: (6.1000 or 6.1210) and (18.03 or 18.06)

Grad · Fall · 3-0-9 units

Fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high-dimensions, and applications to computer vision, natural language processing, and robotics.

Prerequisites: 18.05 and (6.3720, 6.3900, or 6.C01)

Grad · Fall · 3-0-9 units

Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, non-parametric Bayesian methods, hidden Markov models, Bayesian networks, and convolutional and recurrent neural networks.

Prerequisites: 18.06 and (6.3700, 6.3800, or 18.600); 6.3900 or other ML experience recommended

Grad · Fall · 4-0-8 units

Examines reinforcement learning (RL) as a methodology for approximately solving sequential decision-making under uncertainty, with foundations in optimal control and machine learning. Core topics include: dynamic programming, finite and infinite horizon Markov Decision Processes, value and policy iteration, Monte Carlo methods, temporal differences, Q-learning, stochastic approximation, and bandits. Also covers approximate dynamic programming, including value-based methods and policy space methods. Focus is mathematical, but is supplemented with computational exercises.

Prerequisites: 6.3700 or permission of instructor; mathematical maturity is necessary

Undergrad · Fall · 15 units

Study statistical and neural language models, with a substantial final project.

Prerequisites: 6.3900, (6.3700 or 6.3800), and (18.06 or 18.C06)

Spring 2027 · Finale Doshi-Velez

A probabilistic introduction to supervised and unsupervised learning, neural networks, and inference.

Prerequisites: Multivariable calculus, linear algebra, probability, complexity theory, and Python programming

Fall 2026 · Kiante Brantley

Study how agents learn through interaction, including reinforcement-learning problem formulations, algorithms, and theory.

Fall 2026 · Juncheng Yang

Build and evaluate AI agents, then examine serving, caching, quantization, and other systems-level performance trade-offs.

Prerequisites: At least one of Harvard CS 61, CS 1610, or CS 2620; proficiency in Python and PyTorch

Fall 2026 · Finale Doshi-Velez

Investigate whether AI systems improve human outcomes through readings, discussion, and a semester-long project.

Undergrad · Fall · 12 units

Examine AI alignment, existential risk, privacy, fairness, and the ethical choices involved in building computing systems.

Prerequisites: None

Undergrad · Fall · 12 units

Examine how data-driven decisions affect society, including feedback loops and unintended consequences.

Prerequisites: None; corequisite: 6.1200, 6.3700, 6.3800, 18.05, or 18.600

Fall 2026 · Jim Waldo

Examine privacy, surveillance, database anonymity, and the policy and ethical questions raised by technology.

Prerequisites: The course listing describes it as accessible to students across disciplines

Spring 2027 · Cynthia Dwork

Explore mathematical foundations of algorithmic fairness, differential privacy, and statistical validity.

Grad seminar · Fall 2026 · Taught by Boaz Barak

A graduate course on technical and societal AI safety, with lectures, readings, and a group experiment. In-person attendance is required; see the course page for admission requirements.

Prerequisites: Comfort with proofs, probability, and information theory; undergraduate ML (e.g., 6.3900); Python experience training neural networks